The objective of the proposed research is to provide an evidence-base to better inform user centered design and implementation processes to improve health information technology (health IT) usability and safety. The proposed research is in direct response to special emphasis notice (NOT-HS-15-005). Utilizing a hybrid approach of expert manual review and machine learning techniques, specifically natural language processing, we will develop methods to rapidly analyze patient safety event data to determine which events are health IT related. We will then further categorize the health IT related safety events to determine which events could have been prevented by effective usability or implementation processes. Through this analysis we will be able to specify the usability and implementation processes that are critical to the safe and effective use of health IT. This project utilizes the extensive expertise of the research team in human factors and safety science, health IT, and computer science. The proposed research is based on unique insights that our team gleaned from previous research that we conducted focusing on health IT vendor design and implementation processes. The application addresses fundamental aspects of the call for applications by providing an evidence base to improve health IT usability and safety to better inform policy and practice. This research effort is being conducted in partnership with a health IT vendor and a patient safety organization to ensure that our results align with vendor needs and to ensure the results are generalizable. Contributions from this research will include a fundamental understanding of the critical user centered design and implementation processes to inform vendor and provider practice. Our research will also provide organizations like the Office of the National Coordinator with the information to better inform health IT policy.